This is a Plain English Papers summary of a research paper called AI Models Show Different Learning Paths to Abstract Reasoning: Function vs Direct Prediction. If you like these kinds of analysis, you should join AImodels.fyi or follow us on Twitter.
Overview
- The paper explores whether it's better to infer a latent function that explains a few examples, or to directly predict new test outputs using a neural network.
- The experiments are conducted on the ARC dataset, which contains abstract reasoning tasks.
- The models are trained on synthetic data generated by prompting large language models (LLMs) to produce Python code that specifies a function and generates inputs for that function.
Plain English Explanation
The paper looks at two different approaches to learning from a small number of examples. One approach is to try to infer the underlying function that explains the examples. The other approach is to directly predict the outputs for new test inputs, without explicitly mod...
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